Convolutional neural network-based tool condition monitoring in vertical milling operations using acoustic signals

Clayton Cooper, Peng Wang, Jianjing Zhang, Robert X. Gao, Travis Roney, Ihab Ragai, Derek Shaffer

Research output: Contribution to journalConference articlepeer-review

29 Scopus citations

Abstract

Sonic monitoring presents itself as one of the least invasive but easiest to implement methods of machine condition characterization. This work investigates the viability of categorically classifying cutting tool wear using only sonic output from a vertical milling center and proposes a statistical model of milling acoustic signals as well as a novel machine learning-integrated method of acoustic signal differentiation. To this end, a deep convolutional neural network is used for data classification. Experimental results support the proposed sonic model and demonstrate that tool wear classification accuracy as high as 99.5% is possible using a two-dimensional deep convolutional neural network.

Original languageEnglish
Pages (from-to)105-111
Number of pages7
JournalProcedia Manufacturing
Volume49
DOIs
StatePublished - 2020
Event8th International Conference on Through-Life Engineering Services, TESConf 2019 - Cleveland, United States
Duration: Oct 27 2019Oct 29 2019

Bibliographical note

Publisher Copyright:
© 2019 The Authors.

Keywords

  • Acoustic signals
  • Convolutional neural network
  • Tool condition monitoring

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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